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Smoothed Particle Hydrodynamics (SPH) is a unique numerical method widely used for astrophysical problems since it involves no spatial grid. Rather, fluid quantities are carried by a set of Lagrangian `particles' which move with the flow,…

天体物理学 · 物理学 2009-09-29 Daniel Price

The Smoothed Particle Hydrodynamics (SPH) is a particle-based, Lagrangian method for fluid-flow simulations. In this work, fundamental concepts of this method are first briefly recalled. Then, the ability to accurately model granular…

地球物理 · 物理学 2016-02-26 Kamil Szewc

Since its inception, the full Lagrangian meshless smoothed particle hydrodynamics (SPH) method has experienced a tremendous enhancement in methodology and impacted a range of multi-physics applications in science and engineering. The paper…

计算物理 · 物理学 2024-05-24 Chi Zhang , Yujie Zhu , Dong Wu , Xiangyu Hu

This paper presents the Smoothed particle hydrodynamics (SPH) model to examine the propagation of a regular and irregular waves. The SPH method is a grid-less Lagrangian approach which is capable of good accuracy in tracking large…

流体动力学 · 物理学 2009-11-11 D. De Padova , R. A. Dalrymple , M. Mossa , A. F. Petrillo

Smoothed Particle Hydrodynamics (SPH_ is a mesh-free Lagrangian method renowned for modeling large deformations and free-surface flows, yet classical formulations remain confined to deterministic systems. We introduce Stochastic SPH…

计算工程、金融与科学 · 计算机科学 2026-05-14 Mridul Tiwari , Sawan Kumar , Md Rushdie Ibne Islam , Souvik Chakraborty

Smoothed Particle Hydrodynamics (SPH) methods are advantageous in simulations of fluids in domains with free boundary. Special SPH methods have also been developed to simulate solids. However, there are situations where the matter behaves…

流体动力学 · 物理学 2024-01-18 Ondřej Kincl , Ilya Peshkov , Michal Pavelka , Václav Klika

I present a review of Smoothed Particle Hydrodynamics (SPH), with the aim of providing a mathematically rigorous, clear derivation of the algorithms from first principles. The method of discretising a continuous field into particles using a…

天体物理仪器与方法 · 物理学 2010-10-05 Peter J. Cossins

A new variational inference method, SPH-ParVI, based on smoothed particle hydrodynamics (SPH), is proposed for sampling partially known densities (e.g. up to a constant) or sampling using gradients. SPH-ParVI simulates the flow of a fluid…

人工智能 · 计算机科学 2024-07-29 Yongchao Huang

The aim of this paper is to introduce a new computational fluid dynamics method to be called unsmoothed particle hydrodynamics SPH$-i$ which makes few assumptions and makes no assumption beyond the Navier-Stokes equations. The most…

流体动力学 · 物理学 2018-08-01 Kalale Chola

Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving…

流体动力学 · 物理学 2024-07-09 Artur P. Toshev , Jonas A. Erbesdobler , Nikolaus A. Adams , Johannes Brandstetter

Smoothed Particle Hydrodynamics (SPH) is a popular numerical technique developed for simulating complex fluid flows. Among its key ingredients is the use of nonlocal integral relaxations to local differentiations. Mathematical analysis of…

数值分析 · 数学 2019-07-04 Qiang Du , Xiaochuan Tian

The method of smoothed particle hydrodynamics (SPH) is developped appropriately for the study of relativistic heavy ion collision processes. In order to describe the flow of a high energy but low baryon number density fluid, the entropy is…

高能物理 - 唯象学 · 物理学 2008-11-26 C. E. Aguiar , T. Kodama , T. Osada , Y. Hama

In recent years, forest fires and maritime accidents have occurred frequently, which have had a bad impact on human production and life. Thus, the development of seaplanes is an increasingly urgent demand. It is important to study the…

流体动力学 · 物理学 2023-06-22 Yang Xu , Peng-Nan Sun , Xiao-Ting Huang , Salvatore Marrone , Lei-Ming Geng

This review discusses Smoothed Particle Hydrodynamics (SPH) in the astrophysical context, with a focus on inviscid gas dynamics. The particle-based SPH technique allows an intuitive and simple formulation of hydrodynamics that has excellent…

宇宙学与河外天体物理 · 物理学 2011-09-13 Volker Springel

Simulating the flow of different fluids can be a highly computational intensive process, which requires large amounts of resources. Recently there has been a lot of research effort directed towards GPU processing, which can greatly increase…

计算工程、金融与科学 · 计算机科学 2019-07-17 Adrian Coman , Elena Apostol , Catalin Leordeanu , Emil Slusanschi

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian method for solving the fluid equations that is commonplace in astrophysics, prized for its natural adaptivity and stability. The choice of variable to smooth in SPH has been the topic of…

星系天体物理 · 物理学 2021-05-26 Josh Borrow , Matthieu Schaller , Richard G. Bower

Diffuse-interface theory provides a foundation for the modeling and simulation of microstructure evolution in a very wide range of materials, and for the tracking/capturing of dynamic interfaces between different materials on larger scales.…

计算物理 · 物理学 2018-08-03 Zhijie Xu , Paul Meakin , Alexandre Tartakovsky

Smoothed particle hydrodynamics is a particle-based, fully Lagrangian, method for fluid-flow simulations. In this work, fundamental concepts of the method are first briefly recalled. Then, we present a thorough comparison of three different…

流体动力学 · 物理学 2012-08-22 Kamil Szewc , Jacek Pozorski , Jean-Pierre Minier

There have been some issues in the past in attempts to simulate magnetic fields using the Smoothed Particle Hydrodynamics (SPH) method. SPH is well suited to star formation problems because of its Lagrangian nature. We present new, stable…

天体物理学 · 物理学 2009-11-10 D. J. Price , J. J. Monaghan

We present the results from a two-day study in which we discussed various implementations of Smooth Particle Hydrodynamics (SPH), one of the leading methods used across a variety of areas of large-scale astrophysical simulations. In…

天体物理学 · 物理学 2007-05-23 Piet Hut , Lars Hernquist , George Lake , Jun Makino , Steve McMillan , Thomas Sterling
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